code: def _add_classify_op(prog, classifier_config):
      """
      Add a "classify" op to the program, at the end of the main block
      """
  
      def remove_output(block, prob_var):
          for i in range(len(block.outputs)):
              if block.outputs[i] is prob_var:
                  block.outputs.pop(i)
                  break
  
      block = prog.functions["main"]
  
      message = "Class labels must be a list of integers / strings or a file path"
      classes_in = classifier_config.class_labels
      if isinstance(classes_in, str):
          import os
  
          if not os.path.isfile(classes_in):
              raise ValueError(
                  "Path to class labels (%s) does not exist."
                  % classes_in
              )
          with open(classes_in, "r") as f:
              classes = f.read()
          classes = classes.splitlines()
      elif isinstance(classes_in, list):  # list[int or str]
          classes = classes_in
          assert all(
              [isinstance(x, (int, str)) for x in classes]
          ), message
      else:
          raise ValueError(message)
  
      probability_var = _get_probability_var_for_classifier(
          prog, classifier_config
      )
  
      # add the classify op now
      with block:
          # cast the int label to np.int64
          if isinstance(classes[0], int):
              classes = [np.int64(x) for x in classes]
          classes_var = mb.const(val=mil_list(classes))
          if probability_var.dtype != types.fp32:
              remove_output(block, probability_var)
              probability_var = mb.cast(
                  x=probability_var,
                  dtype="fp32",
                  name=probability_var.name + "_cast_to_fp32",
              )
          out = mb.classify(
              probabilities=probability_var, classes=classes_var
          )
  
          predicted_feature_name = (
              "classLabel"
              if classifier_config.predicted_feature_name is None
              else classifier_config.predicted_feature_name
          )
          out[0].name = predicted_feature_name
          out[1].name = predicted_feature_name + "_probs"
  
          # Remove probabilities from block outputs, replace with classify's outputs
          remove_output(block, probability_var)
          block.outputs[:0] = out
          return out[0].name, out[1].name
  
imports: import os
  import warnings
  from typing import Optional
  import numpy
  from coremltools import _OPSET, _SPECIFICATION_VERSION_IOS_15
  from coremltools import _logger
  from coremltools.converters.mil.backend.backend_helper import _get_probability_var_for_classifier
  from coremltools.converters.mil.backend.mil.helper import cast_to_framework_io_dtype, create_file_value, create_immediate_value, create_list_scalarvalue, create_scalar_value, types_to_proto
  from coremltools.converters.mil.backend.nn.load import _set_optional_inputs
  from coremltools.converters.mil.input_types import EnumeratedShapes, ImageType, RangeDim, TensorType
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Program, mil_list, types
  from coremltools.converters.mil.mil.ops.registry import SSAOpRegistry
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic, any_variadic, is_symbolic
  from coremltools.models.neural_network.flexible_shape_utils import NeuralNetworkImageSize, NeuralNetworkImageSizeRange, add_enumerated_image_sizes, add_multiarray_ndshape_enumeration, set_multiarray_ndshape_range, update_image_size_range
  from coremltools.models.utils import _WEIGHTS_FILE_NAME
  from coremltools.proto import FeatureTypes_pb2
  from coremltools.proto import MIL_pb2
  from coremltools.proto import Model_pb2
  from ..backend_helper import _get_colorspace_enum, _validate_image_input_output_shapes
label_desc: Use image node_id 7 for calling a global function with example usage: _add_classify_op(prog, classifier_config) and returns: out,  out